Presenting the ePoster in Glasgow
Abstract
In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we present the implementation of an artifact detection algorithm based on a minimal number of EEG channels on a parallel ultra-low-power (PULP) embedded platform.
Date
14, Jul, 2022
Location
Scottish Event Campus
Exhibition Way, Glasgow, Glasgow G3 8YW
Thorir Mar Ingolfsson
PhD Student in Robust and Practical Machine Learning
My research interests include TinyML, Hardware Aware NAS and application of ML for bio-signals.